Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy

Deng, Xiangyang; Zhang, Limin; Lin, Huan; Luo, Lan

doi:10.1016/j.neucom.2012.12.084

Cited by 16 publications

(2 citation statements)

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“…There are also some studies about reinforcing pheromone trails that cannot be applied in general cases and address only specific situations. In [24], Deng et al proposed a technique for situations when pheromone trails are associated with nodes instead of arcs of the construction graph. The best-so-far strategy is used together with new rules (these new rules are named r-best-node update rule and a relevant-node depositing rule; the first one has a somewhat similar name to our κ-best strategy, although the actual methods are very different) proposed by the authors specifically for this type of problem.…”

Section: Related Workmentioning

confidence: 99%

Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information

2023

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Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that is being solved. By using pheromones, the algorithm builds a probabilistic model that is exploited for constructing new and, hopefully, better solutions. Traditionally, there are two different strategies for updating pheromone trails. The best-so-far strategy (global best) is rather greedy and can cause a too-fast convergence of the algorithm toward some suboptimal solutions. The other strategy is named iteration best and it promotes exploration and slower convergence, which is sometimes too slow and lacks focus. To allow better adaptability of ant colony optimization algorithms we use κ-best, max-κ-best, and 1/λ-best strategies that form the entire spectrum of strategies between best-so-far and iteration best and go beyond. Selecting a suitable strategy depends on the type of problem, parameters, heuristic information, and conditions in which the ACO is used. In this research, we use two representative combinatorial NP-hard problems, the symmetric traveling salesman problem (TSP) and the asymmetric traveling salesman problem (ATSP), for which very effective heuristic information is widely known, to empirically analyze the influence of strategies on the algorithmic performance. The experiments are carried out on 45 TSP and 47 ATSP instances by using the MAX-MIN ant system variant of ACO with and without local optimizations, with each problem instance repeated 101 times for 24 different pheromone reinforcement strategies. The results show that, by using adjustable pheromone reinforcement strategies, the MMAS outperformed in a large majority of cases the MMAS with classical strategies.

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Section: Related Workmentioning

confidence: 99%

Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information

2023

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“…Pheromone trails play a central role in the performance of ACO algorithms. As such, many efforts have been undertaken to develop efficient pheromone strategies by devising appropriate pheromone models/representations [249][250][251], setting appropriate trail limits for the MAX-MIN ant system (MMAS) [252], developing novel techniques for pheromone initialization and updating [253,254], and employing multiple types of pheromones [255,256]. However, all the existing pheromone strategies are problem instance specific, where the pheromone trails generated for one instance cannot be used for another instance.…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria journey planning for enhancing user experience in multimodal transportation systems

He¹

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Although the PhD journey is very tough, I am extremely lucky to have limitless kind helps from my supervisors, my family, and my friends. I would like to express my deepest gratitude to everyone for supporting me throughout my PhD research. First, I would like to sincerely express my gratitude to my supervisor Prof. Lam Siew Kei, for his encouragement, patience, motivation, enthusiasm, and continuous support throughout my PhD candidature. His countless guidance enables me to have a better understanding of my research area. It is a great honor for me to be one of Prof. Lam Siew Kei's Ph.D. students and obtain his personal guidance. I also would like to give heartfelt thanks to Prof. Thambipillai Srikanthan for his support of my PhD project. With his long experience and deep expertise in intelligent transportation areas, Prof. Srikanthan's suggestions and comments have been crucial in improving the practical relevance of my work.My deepest gratitude also goes to my husband, who is also a researcher in computer science, for his not only helping me to be better equipped for research but also unconditional love and encouragement to me. He always gives me valuable advice that I can orientate my research direction and grasp necessary techniques. Also, I feel very thankful to my dearest parents for their constant love, support, and understanding. Especially they spent nearly all their time to help me look after my daughter throughout my PhD study. Their supports gives me endless energy and lets me continue to advance.

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An Improved Ant Colony Optimization with Subpath-Based Pheromone Modification Strategy

Deng

Zhang

Feng

2017

Lecture Notes in Computer Science

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Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy

Cited by 16 publications

References 13 publications

Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information

Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information

Multi-criteria journey planning for enhancing user experience in multimodal transportation systems

An Improved Ant Colony Optimization with Subpath-Based Pheromone Modification Strategy

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